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Archive of posts filed under the Causal Inference category.

This is your chance to comment on the U.S. government’s review of evidence on the effectiveness of home visiting. Comments are due by 1 Sept.

Emily Sama-Miller writes: The federally sponsored Home Visiting Evidence of Effectiveness (HomVEE) systematic evidence review is seeking public comment on proposed updates to its standards and procedures. HomVEE reviews research literature on home visiting for families with pregnant women and children from birth to kindergarten entry, and its results are used to inform federal funding […]

Somethings do not seem to spread easily – the role of simulation in statistical practice and perhaps theory.

Unlike Covid19, somethings don’t seem to spread easily and the role of simulation in statistical practice (and perhaps theory) may well be one of those. In a recent comment, Andrew provided a link to an interview about the new book Regression and Other Stories by Aki Vehtari, Andrew Gelman, and Jennifer Hill. An interview that covered […]

“100 Stories of Causal Inference”: My talk tomorrow at the Online Causal Inference Seminar

Tues 4 Aug, 11:30am on zoom: 100 Stories of Causal Inference In social science we learn from stories. The best stories are anomalous and immutable. We shall briefly discuss the theory of stories, the paradoxical nature of how we learn from them, and how this relates to forward and reverse causal inference. Then we will […]

“The Taboo Against Explicit Causal Inference in Nonexperimental Psychology”

Kevin Lewis points us to this article by Michael Grosz, Julia Rohrer, and Felix Thoemmes, who write: Causal inference is a central goal of research. However, most psychologists refrain from explicitly addressing causal research questions and avoid drawing causal inference on the basis of nonexperimental evidence. We argue that this taboo against causal inference in […]

BMJ update: authors reply to our concerns (but I’m not persuaded)

Last week we discussed an article in the British Medical Journal that seemed seriously flawed to me, based on evidence such as the above graph. At the suggestion of Elizabeth Loder, I submitted a comment to the paper on the BMJ website. Here’s what I wrote: I am concerned that the model does not fit […]

The importance of descriptive social science and its relation to causal inference and substantive theories

Here’s the abstract to a recent paper, Escaping Malthus: Economic Growth and Fertility Change in the Developing World, by Shoumitro Chatterjee and Tom Vogl: Following mid-twentieth century predictions of Malthusian catastrophe, fertility in the developing world more than halved, while living standards more than doubled. We analyze how fertility change related to economic growth during […]

Would we be better off if randomized clinical trials had never been born?

This came up in discussion the other day. In statistics and medicine, we’re generally told to rely when possible on the statistically significance (or lack of statistical significance) of results from randomized trials. But, as we know, statistical significance has all sorts of problems, most notably that it ignores questions of cost and benefit, and […]

Please socially distance me from this regression model!

A biostatistician writes: The BMJ just published a paper using regression discontinuity to estimate the effect of social distancing. But they have terrible models. As I am from Canada, I had particular interest in the model for Canada, which is on their supplemental material, page 84 [reproduced above]. I could not believe this was published. […]

Association Between Universal Curve Fitting in a Health Care Journal and Journal Acceptance Among Health Care Researchers

Matt Folz points us to this recent JAMA article that features this amazing graph: Beautiful. Just beautiful. I say this ironically.

Further debate over mindset interventions

Warne Following up on this post, “Study finds ‘Growth Mindset’ intervention taking less than an hour raises grades for ninth graders,” commenter D points us to this post by Russell Warne that’s critical of research on growth mindset. Here’s Warne: Do you believe that how hard you work to learn something is more important than […]

“To Change the World, Behavioral Intervention Research Will Need to Get Serious About Heterogeneity”

Beth Tipton, Chris Bryan, and David Yeager write: The increasing influence of behavioral science in policy has been a hallmark of the past decade, but so has a crisis of confidence in the replicability of behavioral science findings. In this essay, we describe a nascent paradigm shift in behavioral intervention research—a heterogeneity revolution—that we believe […]

Adjusting for Type M error

Erik Drysdale discusses and gives some formulas, demonstrating on an example that will be familiar to regular readers of this blog.

Coronavirus jailbreak

Emma Pierson writes: My two sisters and I, with my friend Jacob Steinhardt, spent the last several days looking at the statistical methodology in a paper which has achieved a lot of press – Incarceration and Its Disseminations: COVID-19 Pandemic Lessons From Chicago’s Cook County Jail (results in supplement), published in Health Affairs. (Here’s the […]

Regression and Other Stories is available!

This will be, without a doubt, the most fun you’ll have ever had reading a statistics book. Also I think you’ll learn a few things reading it. I know that we learned a lot writing it. Regression and Other Stories started out as the first half of Data Analysis Using Regression and Multilevel/Hierarchical Models, but […]

No, I don’t believe that claim based on regression discontinuity analysis that . . .

tl;dr. See point 4 below. Despite the p-less-than-0.05 statistical significance of the discontinuity in the above graph, no, I do not believe that losing a close election causes U.S. governors to die 5-10 years longer, as was claimed in this recently published article. Or, to put it another way: Despite the p-less-than-0.05 statistical significance of […]

The value of thinking about varying treatment effects: coronavirus example

Yesterday we discussed difficulties with the concept of average treatment effect. Part of designing a study is accounting for uncertainty in effect sizes. Unfortunately there is a tradition in clinical trials of making optimistic assumptions in order to claim high power. Here is an example that came up in March, 2020. A doctor was designing […]

Understanding the “average treatment effect” number

In statistics and econometrics there’s lots of talk about the average treatment effect. I’ve often been skeptical of the focus on the average treatment effect, for the simple reason that, if you’re talking about an average effect, then you’re recognizing the possibility of variation; and if there’s important variation (enough so that we’re talking about […]

The point here is not the face masks; it’s the impossibility of assumption-free causal inference when the different treatments are entangled in this way.

Adam Pearce writers: When I read your Another Regression Discontinuity Disaster post last year, I was curious how much shifting the breakpoint would change the fit lines. A covid paper making the rounds this weekend used a similar technique so I hooked it up to an interactive widget that lets you tweak the start and […]

Challenges to the Reproducibility of Machine Learning Models in Health Care; also a brief discussion about not overrating randomized clinical trials

Mark Tuttle pointed me to this article by Andrew Beam, Arjun Manrai, and Marzyeh Ghassemi, Challenges to the Reproducibility of Machine Learning Models in Health Care, which appeared in the Journal of the American Medical Association. Beam et al. write: Reproducibility has been an important and intensely debated topic in science and medicine for the […]

How should those Lancet/Surgisphere/Harvard data have been analyzed?

As you will recall, the original criticism of the recent Lancet/Surgisphere/Harvard paper on hydro-oxy-whatever was not that the data came from a Theranos-like company that employs more adult-content models than statisticians, but rather that the data, being observational, required some adjustment to yield strong causal conclusions—and the causal adjustment reported in that article did not […]